Abstract
Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.
Highlights
As one of the most promising developments in remote sensing imagery, the satellite videos captured by Skybox and JL-1, have facilitated several emerging research and applications, including super resolution [1,2], video encoding [3,4] and target tracking [5,6]
The semantic regions are allowed to be larger than the target size on purpose
The SkySat-Burj Khalifa dataset refers to the satellite video, which is captured over Burj Khalifa, United Arab Emirates on April, 2014
Summary
As one of the most promising developments in remote sensing imagery, the satellite videos captured by Skybox and JL-1, have facilitated several emerging research and applications, including super resolution [1,2], video encoding [3,4] and target tracking [5,6] They expand the earth observation capacity to rapid motion monitoring, such as vehicle and ship tracking [5,7,8]. Detecting objects of interest in a video can be achieved by the motion-based detectors, which search the changed pixels in a sequence of images by comparing with an estimated background model [9,10] Various algorithms, such as Frame-Difference [5,11,12], Median Background [13], Gaussian Mixture Model (GMM) [14,15] and Visual Background Extractor (ViBe) [7,16,17], were developed for moving object detection. These approaches are prone to the inadequate background modelling and affected by the problem of parallax caused by the motion of the camera
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
